The present disclosure generally relates to decision-support methods that are used by consumer-product manufacturers and/or consumer store and retail chains for a variety of retail applications in the areas of inventory optimization, product pricing, product-line rationalization and promotion planning; more particularly, the present invention relates to a system and method for accurate demand modeling and prediction in retail categories.
Demand models are an important component of several retail decision-support applications used by various entities in a retail supply chain including consumer product manufacturers and/or consumer retail chains and individual retail stores. Some examples of retail applications that require accurate demand models for individual products, or for entire retail categories, include, for instance, inventory optimization, product pricing, product-line rationalization, and promotion planning. As a specific example, the architecture of a retail pricing decision-support system is discussed in A. L. Montgomery, “The Implementation Challenge of Pricing Decision Support Systems for Retail Managers, Applied Stochastic Methods in Business and Industry, Vol. 27(4-5), pp. 367-378, 2005, which provides a detailed rationale for demand models for retail categories in that specific context.
In the broader context of these retail decision-support applications, it would be highly desirable to provide a system and method for obtaining more accurate and comprehensive models for the demand modeling component that, as a result, yields significant improvements in the performance and effectiveness of the overall decision-support application.
Various approaches have been considered for retail demand modeling in the prior art. In particular, in methods that are embedded in existing commercial products, it is discerned that the methods for retail demand modeling in the prior art are invariably based on univariate time-series analysis approaches, wherein, each product is analyzed separately and independently from other products, rather than jointly, even for competing products within the same retail product category.
Furthermore, the methods in the prior art are invariably based on the direct time-series analysis of the unit-sales data itself (note that this time-series analysis is performed with the transactional unit-sales data being aggregated to the time-series of daily, weekly or quarterly unit sales, in accordance and consistent with the sales and replenishment reporting cycles for the chosen product). For instance, many approaches for demand modeling in the prior art are based on the Holt-Winters exponential smoothing algorithm for univariate time series, which is particularly suitable for retail data which contains trend and seasonality effects. A background description of time-series methods, including the methods that have been widely adopted in prior art for demand modeling, can be found in P. J. Brockwell and R. J. Davis, “Introduction to Time Series and Forecasting,” Springer-Verlag, New York, 2002.
The univariate time-series methods, as described above, are often augmented by certain multivariable extensions to the basic methodology for demand modeling applications. For example, a variety of relevant causal factors can be included in the methods of the prior art for demand modeling, wherein the relevant causal factors, which consist of known and specified auxiliary time-series of variables which are included in the modeling, since these variables are known to influence the time series for the primary variable that needs to be forecasted, in this case, the unit-sales time series for the chosen product of interest. In principle, there are no restrictions on the choice of relevant causal factors that can be included in this approach for the demand modeling analysis; in practice, however, these causal factors are usually endogenous to the chosen product of interest, or equivalently, the causal factors are restricted to attributes of the chosen product of interest itself (for example, the corresponding unit-price time series data in each sales-reporting period is usually a causal factor for predicting the unit-sales sequence for the chosen product of interest; in addition, other endogenous causal factors will include the corresponding promotion codes and/or inventory stock-out codes for the given product in each sales reporting period).
Stated alternatively, these demand model and forecasts in the prior art do not typically incorporate any causal factors that are associated with the price or product characteristics of any of the competing products in the retail category, although the accuracy of the resulting demand models can often be significantly improved by incorporating the relevant causal factors that are exogenous to the chosen product of interest.
Two reasons are discerned for the demand modeling methods in the prior art being limited to univariate time series methods, or to extensions of univariate time series methods that only incorporate endogenous causal factors:
The first reason is particularly relevant for the case of demand models used by consumer-product manufacturer; here, it is noted that the eventual unit-price at which chosen product is sold to the customer is typically fixed by either the retail chain or the individual retail store, and the markup and discount policies that are used to set the retail price are not always known to the consumer-product manufacturer. In this case, therefore, the actual unit-price for the product sales will need to be obtained, often at a significant expense, either from market audits and surveys or from third-party information aggregators. Even in the case of vendor-managed inventory products, where the consumer-product manufacturer has access to the eventual point-of-sales data, the corresponding unit-price data for any competitor products in the same individual retail chain or store, is often not directly available to the consumer-product manufacturer, and this competitor sales data, therefore, will need to be obtained, often at significant expense, from the individual retail chain and store, or from third-party information aggregators. These data-availability issues may only be germane to consumer-product manufacturers, or to other intermediate supply-chain entities who do not directly market their products to the end-customer; however, retail chains or individual retail stores that interact directly with the end-customer also face similar difficulties in procuring the relevant sales data from other retail chains, and therefore, as noted above, this data will need to be obtained at significant expense from third-party information aggregators.
In the absence of the sales and demand data from a larger set of retail chains, in some cases, it may not be possible to obtain statistically-significant estimates of the demand model parameters, particularly for more-complex demand models which incorporate numerous parameters as a consequence of using an extensive set of causal factors based on competitive product attributes or sales data.
The second reason is that even when the unit-sales and unit-price data for competitor products in the retail category is available, thereby allowing and extensive set of competing sales and product attributes to be incorporated in the demand modeling using the causal factor approach as envisioned above, an additional complication arises when the resulting demand models are used for subsequent prediction or forecasting. This complication is due to the fact that some of the competitor product attributes used as causal factors in the demand model, including for example the unit-prices and promotion codes for competitor products, are “non-controllable” factors; in effect, the inclusion of these exogenous product attributes as causal factors in the time-series demand model leads to the situation that these values are not explicitly known in a prediction or forecasting scenario, and furthermore, requiring assumptions on their values will invariably limit the accuracy of the demand models for prediction or forecasting.
In summary, the methods for demand modeling in the prior art are limited, particularly, in terms of incorporating all relevant and potentially-important, competing-product effects as causal factors in the demand modeling analysis.
In addition, these methods in the prior art do not typically provide a joint model for all the competing products in the retail category, which is necessary in order to capture the product substitution and cross-elasticity effects in certain retail categories.
Therefore, this leads to inherent limitations in the accuracy of the methods for demand modeling in the prior art, and to inherent limitations in their usefulness for decision-support applications, particularly in cases where competing product effects and product substitution are significant factors within a retail category. In fact, most, if not all, retail decision-support applications, would significantly benefit from incorporating competitor-product data and attributes in the demand modeling, and furthermore, the inclusion of these effects is central to several decision-support applications where accurate demand modeling is required, such as product cannibalization, product rationalization, joint promotions and strategic pricing.